Data-Aided MIMO Channel Estimation by Clustering and Reinforcement-Learning

In this paper, we propose a data-aided channel estimator, which can improve the performance of the linear minimum-mean-squared-error (LMMSE) by clustering and reinforcement-learning for multiple-input multiple-output (MI-MO) systems. For clustering-based data detection, we develop a system constrain...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC s. 584 - 589
Hlavní autoři: Li, Xing, Wang, Qianfan, Yang, Hongqi, Ma, Xiao
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 10.04.2022
Témata:
Channel estimation
Clustering algorithms
Encoding
Modulation
Phase shift keying
Probability
Symbols
ISSN:1558-2612
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this paper, we propose a data-aided channel estimator, which can improve the performance of the linear minimum-mean-squared-error (LMMSE) by clustering and reinforcement-learning for multiple-input multiple-output (MI-MO) systems. For clustering-based data detection, we develop a system constrained Gaussian mixture model (SCGMM), in which the a posteriori probabilities (APPs) can be calculated by the expectation-maximization (EM) algorithm. The initial centroids of the SCGMM are sensitive to the channel estimation. To obtain robust channel estimation, we design initial pilots that can reduce the estimated error of the LMMSE. To further improve the quality of channel estimation, we propose a data-aided channel estimation algorithm, which exploits the techniques of coding and reinforcement-learning to obtain soft symbol decisions. Numerical results show that the proposed method can approach the bit-error-rate (BER) performance with perfect channel state information (CSI) in the high signal-to-noise (SNR) region.
ISSN:1558-2612
DOI:10.1109/WCNC51071.2022.9771693